Power equipment data uploading and classification processing method and system
Through hierarchical division and edge control treatment of power equipment, combined with the verification of unmanned inspection equipment and the update of virtual simulation models, the problem of data collection, processing and transmission in the power equipment monitoring system, realized the operating status of the equipment, and achieved the operating status of the equipment. Monitor the security and stability of the system accurately.
Patent Information
- Application Number
- CN202411519944.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-29
- Publication Date
- 2025-01-24
AI Technical Summary
When facing a variety of equipment and complex topological structures, existing power equipment monitoring systems are difficult to efficiently collect, process and transmit data, resulting in high operating costs, frequent false alarms, unscientific patrol inspections, and difficulty in quickly locate abnormal equipment.
By dividing power equipment into different levels and nodes according to type, topology relationship and priority, using sensors to obtain node working parameters, the edge controller processes and sends the data based on the amplitude comparator, determines the equipment operating status and sensor abnormalities, and activates Unmanned inspection equipment is checked and maintained, and the virtual simulation model is updated.
It achieves accurate monitoring of the operating status of power equipment, avoids false alarms, reduces operating costs, and improves system security and stability.
Smart Images
Figure CN119362708A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of data transmission and processing of electric power equipment, and in particular to a method and system for data transmission and classification processing of electric power equipment. Background Art
[0002] The operational stability of power systems is closely linked to the economic benefits of power companies. In recent years, power system monitoring has become increasingly important to ensure the safety and stability of power system operations. Power monitoring systems typically utilize sensors to collect operating data from various devices within the power system and transmit this data to a monitoring center via communication equipment. The monitoring center analyzes this data to determine the operating status of the power system and, based on the results of this data analysis, enables remote control and management of power equipment.
[0003] The existing power equipment monitoring system mainly has the following problems: First, the types and quantities of power equipment contained in the power system are numerous, and the corresponding parameter types and data volumes that need to be collected and uploaded are also large. This not only puts tremendous pressure on data collection, storage, and transmission, but also generates a large amount of energy consumption, increasing the operating cost of the power equipment monitoring system; Second, in order to realize the collection of different types of parameter data of power equipment, more sensors need to be deployed, but the working environment of power equipment is complex, and sensor hardware failures, network disconnections, etc. will affect the accuracy of data collection and status monitoring, which often leads to false alarms of power equipment anomalies; In addition, the power system structure is complex and often includes a large number of trunk lines and branches, each of which includes multiple devices. How to obtain the operating performance and error conditions of each trunk line and branch to realize scientific and effective inspection and operation and maintenance is also a major problem that needs to be solved in this field; Finally, when an anomaly is discovered, how to quickly and accurately locate the abnormal equipment is also of great significance to ensuring the safety and stability of the power system. Summary of the Invention
[0004] The purpose of the present invention is to provide a method and system for uploading and classifying data of power equipment to solve the technical problems mentioned in the above background technology.
[0005] In order to achieve the above object, the specific technical solution adopted by the present invention is: A method for uploading and classifying power equipment data includes the following steps: S1: Divide power equipment into different levels and nodes based on their type, topology, and priority. S2: Use sensors to obtain the operating parameters of the node. The edge controller processes the obtained operating parameters based on the amplitude comparator and sends them to the server. S3: Determine the operating status of each node device and whether there is a sensor abnormality based on the transfer function of each node and the processed working parameters; S4: Determine whether the abnormal node has a subordinate node. If so, activate the corresponding sensor to obtain the working parameters of the subordinate node, detect whether the subordinate node is abnormal, and locate it. S5: Generate inspection tasks based on the abnormal location information and abnormal type, and dispatch unmanned inspection equipment to verify and maintain abnormal nodes; S6: updating the operating status of each node device to the virtual simulation model; Optionally, the step S2: using a sensor to obtain operating parameters of the node, and the edge controller processing the obtained operating parameters based on an amplitude comparator and sending the obtained parameters to an upstream node, includes: S21: Using sensors to obtain operating parameters of each node in the first level, and sending the operating parameters to a cache module of the edge controller; S22: Using an amplitude comparator to detect the amplitude of the operating parameter in real time, when the amplitude of the operating parameter is greater than a first threshold or less than a second threshold, the amplitude comparator outputs a first signal; when the amplitude of the operating parameter is between the first threshold and the second threshold, the amplitude comparator outputs a second signal; S23: Based on the time domain signals including the first signal and the second signal output by the amplitude comparator, the edge controller retains and marks the node operating parameters for the time period corresponding to the first signal, deletes the operating parameters for the time period corresponding to the second signal, and fills in the parameters with the time period and the average amplitude. S24: Obtain and combine the operating parameters of the first signal and the second signal for the corresponding time period, and send the obtained operating parameters to the remote monitoring center; Optionally, the first threshold = node transfer function f(x) * node actual input x0 * k1; the second threshold = node transfer function f(x) * node actual input x0 * k2; k1 and k2 are the maximum amplitude proportional coefficient and the minimum amplitude proportional coefficient respectively; k1=h1(a, b, c, d); k2=h2(a, b, c, d); Where a, b, c, and d represent the node level, the priority of nodes at the same level, the number of subordinate nodes under the node, and the aging degree of the device corresponding to the node, respectively; h1 and h2 represent the calculation functions of the maximum amplitude proportional coefficient and the minimum amplitude proportional coefficient, respectively; Optionally, step S23 specifically includes: S231: Determine a first time period set T1 = {t1-t2, t3-t4, ...} corresponding to the amplitude comparator outputting the first signal and a second time period set T2 = {t2-t3, t4-t5, ...} corresponding to the amplitude comparator outputting the second signal; S232: retaining the operating parameters of the node acquired by the sensor corresponding to the first time period set and marking them with the first signal label, calculating the average value of the operating parameters of the node acquired by the sensor corresponding to the second time period set, deleting the operating parameters of the corresponding set of the second time period set, and replacing the operating parameters of the node with the average value of the second time period set and the corresponding time period; Optionally, the S1: dividing the power equipment into different levels and nodes based on the type, topology relationship and priority of the power equipment includes: S11: Preliminarily determine the priority of power equipment based on its type and topological relationship, and submit it to the backend staff for verification and adjustment; S12: Divide the power equipment into different levels, each level includes different nodes, and each node corresponds to different power equipment; Optionally, the step S3: judging the operating status of each node device and whether there is a sensor abnormality based on the transfer function of each node and the processed working parameters, includes: S31: During the trial operation phase, the input and output data samples of each node are obtained by changing the switch matrix state, and the transfer function of each node is identified based on the sample data of each node; S32: Detecting the processed working parameters, and when the first signal tag is found, determining that there is a potential abnormality in the operating state of the current node; S33: Based on the topological relationship of the nodes, the output data of the previous node and the next node of the current node are obtained. The ideal output data of the next node is calculated through the transfer function of the current node and the next node and the output data of the previous node. The actual output data of the next node is obtained, and the ideal and actual output data of the next node are compared. When the deviation between the two is within the preset range, it is prompted that the sensor corresponding to the output data of the current node is faulty; when the deviation between the two exceeds the preset range, it is prompted that the operating status of the current node is abnormal.
[0006] According to another aspect of the present invention, a system for uploading and classifying power equipment data is provided, which includes: a remote monitoring center, power equipment 1-n, a cloud server, an edge controller, unmanned inspection equipment, a preset terminal, and a data acquisition module; The remote monitoring center can divide power equipment into different levels and nodes based on the type, topology, and priority of the power equipment; determine the operating status of each node device and whether there is a sensor anomaly based on the transfer function and processed working parameters of each node; determine whether the abnormal node has a subordinate node, and if so, activate the corresponding sensor to obtain the working parameters of the subordinate node, detect whether the subordinate node has an anomaly and locate it; generate inspection tasks based on the location information and type of the anomaly, and dispatch unmanned inspection equipment to verify and maintain the abnormal node; and update the operating status of each node device to the virtual simulation model; The edge controller is arranged at the end of the power equipment; the edge controller can obtain the working parameters of the node by using the sensor, and the edge controller processes the obtained working parameters based on the amplitude comparator and sends them to the upper layer; Optionally, the edge controller can use a sensor to obtain operating parameters of the node. The edge controller processes the obtained operating parameters based on an amplitude comparator and sends them to an upstream node, including: Using sensors to obtain the operating parameters of each node in the first level, and sending the operating parameters to the cache module of the edge controller; An amplitude comparator is used to detect the amplitude of the operating parameter in real time. When the amplitude of the operating parameter is greater than a first threshold or less than a second threshold, the amplitude comparator outputs a first signal. When the amplitude of the operating parameter is between the first threshold and the second threshold, the amplitude comparator outputs a second signal. Based on the time domain signals including the first signal and the second signal output by the amplitude comparator, the edge controller retains and marks the node operating parameters of the time period corresponding to the first signal, deletes the operating parameters of the time period corresponding to the second signal, and fills it with the time period and the average amplitude; Obtaining and combining the operating parameters of the first signal and the second signal for the corresponding time period, and sending the obtained operating parameters to the remote monitoring center; Optionally, the cache module will periodically delete the data therein, and different cache module deletion cycles are set for nodes of different levels and priorities, and nodes with different numbers of subordinate nodes; Optionally, after the unmanned inspection equipment moves to the corresponding position of the fault sensor, the working parameters are collected based on the same type of sensors carried by the unmanned inspection equipment itself for continued monitoring of the operating status of the power equipment. The unmanned inspection equipment can temporarily stay at the position of the fault sensor as needed.
[0007] The present invention provides a method and system for uploading and classifying data of power equipment, including: dividing the power equipment into different levels and nodes based on the type, topological relationship and priority of the power equipment; using sensors to obtain the working parameters of the nodes, and the edge controller processes the obtained working parameters based on the amplitude comparator and uploads them; judging the operating status of each node device and whether there is a sensor abnormality based on the transfer function of each node and the processed working parameters; judging whether the abnormal node has a lower-level node, and activating the corresponding sensor to obtain the working parameters of the lower-level node when it exists, detecting whether there is an abnormality in the lower-level node and locating it; generating inspection tasks based on the location information and type of the abnormality, and dispatching unmanned inspection equipment to calibrate and maintain the abnormal node; the present invention can realize accurate monitoring of the operating status of power equipment, avoid the occurrence of false alarms, and effectively ensure the safety and stability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS
[0008] Figure 1 This is a flow chart of a method for uploading and classifying data of power equipment; Figure 2 This is a schematic diagram of the hierarchical structure of power equipment; Figure 3 This is a schematic diagram of the working principle of the window comparator. DETAILED DESCRIPTION
[0009] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. The specific embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
[0010] As described in the background technology section, the amount and types of equipment data that need to be collected in the power monitoring system are relatively large. The collection, storage, and transmission of massive data are extremely difficult. In addition, sensor aging, failure, and disconnection will affect the accuracy of data collection and status monitoring. In order to achieve accurate monitoring of power equipment, the present invention proposes the following technical solutions: A method for uploading and classifying data of power equipment, such as Figure 1 As shown, it includes the following steps: S1: Divide power equipment into different levels and nodes based on their type, topology, and priority. S2: Use sensors to obtain the operating parameters of the node. The edge controller processes the obtained operating parameters based on the amplitude comparator and sends them to the server. S3: Determine the operating status of each node device and whether there is a sensor abnormality based on the transfer function of each node and the processed working parameters; S4: Determine whether the abnormal node has a subordinate node. If so, activate the corresponding sensor to obtain the working parameters of the subordinate node, detect whether the subordinate node is abnormal, and locate it. S5: Generate inspection tasks based on the abnormal location information and abnormal type, and dispatch unmanned inspection equipment to verify and maintain abnormal nodes; S6: Update the operating status of each node device to the virtual simulation model.
[0011] S1: Based on the type, topology and priority of the power equipment, the power equipment is divided into different levels and nodes, such as Figure 2 As shown, including: S11: Preliminarily determine the priority of power equipment based on its type and topological relationship, and submit it to the backend staff for verification and adjustment; S12: Divide the power equipment into different levels, each level includes different nodes, and each node corresponds to different power equipment; Power equipment can be high-voltage transmission equipment, transformers, low-voltage substation equipment, switchgear, etc., which can constitute level one. Transformers can also include mutual inductance coils, protection equipment, etc., and the corresponding transformer node can include multiple lower-level nodes. S2: Using sensors to obtain the operating parameters of the node, the edge controller processes the obtained operating parameters based on the amplitude comparator and sends them to the server, including: S21: Using sensors to obtain operating parameters of each node in the first level, and sending the operating parameters to a cache module of the edge controller; The operating parameters may be current, voltage, power, temperature, humidity, or smoke concentration, and the corresponding sensors may be current sensors, voltage sensors, power sensors, temperature sensors, humidity sensors, or smoke concentration sensors. Other types of operating parameters and sensors may also be used, and are not specifically limited here. When the power system is operating normally, the sensors may obtain actual input and output data of each node. The edge controller is set at the power equipment end and can be used to collect, process, and send corresponding operating parameters, and receive, process, and send corresponding local or remote control instructions; optionally, an edge controller can be set at each level; The cache module is a temporary storage module with a first-in-first-out and periodic deletion mechanism. The staff can select the memory size of the cache module and set an appropriate deletion cycle according to actual needs. Optionally, the staff can modify the deletion cycle by inputting corresponding instructions to the edge controller. Optionally, different cache module deletion cycles can be set for nodes of different levels and priorities, as well as nodes with different numbers of subordinate nodes, to minimize data loss. like Figure 3 As shown, S22: using an amplitude comparator to detect the amplitude of the operating parameter in real time, when the amplitude of the operating parameter is greater than a first threshold or less than a second threshold, the amplitude comparator outputs a first signal, when the amplitude of the operating parameter is between the first threshold and the second threshold, the amplitude comparator outputs a second signal; The amplitude comparator is arranged at the end of the power equipment and can detect the amplitude of the working parameter; Optionally, the first threshold value U1 and the second threshold value U2 are pre-set, and U1>U2; for those skilled in the art, when the amplitude of the working parameter is greater than the first threshold value or less than the second threshold value, it often corresponds to an abnormal working state of the node. At this time, the real-time detected working parameters of this part should be retained to facilitate accurate judgment of the abnormality and subsequent data tracing; when the amplitude of the working parameter is between the first threshold value and the second threshold value, it is highly likely that the working state of the corresponding node is normal. The actual effect of this part of the real-time detected working parameters is not great, but it occupies a large amount of memory and transmission bandwidth. It can be considered to be deleted or simplified and replaced; Optionally, the first threshold = node transfer function f(x) * node actual input x0 * k1; the second threshold = node transfer function f(x) * node actual input x0 * k2; k1 and k2 are the maximum amplitude proportional coefficient and the minimum amplitude proportional coefficient respectively; The node transfer function refers to the relationship function between the input and output of the node and the power equipment. Different nodes correspond to different power equipment and have different node transfer functions f(x). When the actual input of the node is x0, the node's ideal output can be calculated by the node's transfer function f(x), y0=f(x)*x0. The node's transfer function can also be used to determine the node's operating status and error. Optionally, k1=h1(a, b, c, d); k2=h2(a, b, c, d); Where a, b, c, and d represent the node's level, the priority of nodes at the same level, the number of subordinate nodes under the node, and the aging level of the node's corresponding equipment, respectively. h1 and h2 represent the calculation functions of the maximum amplitude proportional coefficient and the minimum amplitude proportional coefficient, respectively. In other words, k1 and k2 are related to the node's level, the priority of nodes at the same level, the number of subordinate nodes under the node, and the aging level of the node's corresponding equipment. The node level and the number of subordinate nodes under the node are pre-stored based on a mapping table. It is understood that the higher the node level, the more subordinate nodes it has, and the higher its security requirements. The priority of the nodes at the same level is also the priority of the aforementioned power equipment. The aging degree of the equipment is related to the average cumulative amount of errors per unit time and the continuous operating time of the equipment. Specifically: the aging degree of the node device = the continuous operation time of the node device * c1 + the mean value of the node error per unit time * c2; The c1 and c2 are preset proportional coefficients; the mean value of the node error per unit time is calculated by integrating the difference between the ideal output and the actual output of the node and dividing the result by the time; The aging degree of the node device can be periodically updated and calculated while using sensors to obtain the node's operating parameters. The edge controller processes the obtained operating parameters based on the amplitude comparator and sends them to the server. The update period is positively correlated with the node's level. Optionally, the staff can also manually set and adjust the maximum amplitude proportional coefficient and the minimum amplitude proportional coefficient; S23: Based on the time domain signals including the first signal and the second signal output by the amplitude comparator, the edge controller retains and marks the node operating parameters for the time period corresponding to the first signal, deletes the operating parameters for the time period corresponding to the second signal, and fills in the parameters with the time period and the average amplitude. Specifically, it includes: S231: Determine a first time period set T1 = {t1-t2, t3-t4, ...} corresponding to the amplitude comparator outputting the first signal and a second time period set T2 = {t2-t3, t4-t5, ...} corresponding to the amplitude comparator outputting the second signal; S232: retaining the operating parameters of the node acquired by the sensor corresponding to the first time period set and marking them with the first signal label, calculating the average value of the operating parameters of the node acquired by the sensor corresponding to the second time period set, deleting the operating parameters of the corresponding set of the second time period set, and replacing the operating parameters of the node with the average value of the second time period set and the corresponding time period; Calculating the average value of the operating parameters of the nodes obtained by the sensors corresponding to the second time period set specifically involves integrating the time domain information of the operating parameters within the second time period set and dividing it by the time period length to obtain the average value of the operating parameters of the nodes in the time period. This task is completed by the edge controller. S24: Obtain and combine the operating parameters of the first signal and the second signal for the corresponding time period, and send the obtained operating parameters to the remote monitoring center; In this way, the edge controller processes the acquired operating parameters based on the amplitude comparator, retaining operating parameters with amplitudes greater than a first threshold or less than a second threshold, deleting operating parameters with amplitudes between the first and second thresholds, and replacing the operating parameters of nodes with amplitudes between the first and second thresholds with the average value of the corresponding time period. This significantly reduces the amount of data that needs to be stored and uploaded, lowering the operating cost of the power equipment monitoring system. S3: judging the operating status of each node device and whether there is a sensor abnormality based on the transfer function of each node and the processed working parameters, including: S31: During the trial operation phase, the input and output data samples of each node are obtained by changing the switch matrix state, and the transfer function of each node is identified based on the sample data of each node; Optionally, during the actual operation phase, input and output data samples of each node may be periodically obtained to iteratively identify the transfer function and improve the accuracy of operation status monitoring; S32: Detecting the processed working parameters, and when the first signal tag is found, determining that there is a potential abnormality in the operating state of the current node; S33: Based on the topological relationship of the nodes, the output data of the previous node and the next node of the current node are obtained. The ideal output data of the next node is calculated by using the transfer function of the current node, the next node and the output data of the previous node. The actual output data of the next node is obtained. The ideal and actual output data of the next node are compared. When the deviation between the ideal and actual output data is within a preset range, a fault is indicated for the sensor corresponding to the output data of the current node. When the deviation between the ideal and actual output data is outside the preset range, an abnormal operation state of the current node is indicated. In this way, the topological relationship of the nodes and the operating parameters of the upstream and downstream nodes can be used to assist in judging the operating status of the current node, improve the accuracy of data collection and status monitoring, and avoid false alarms of power equipment abnormalities caused by sensor failures; S4: determining whether the abnormal node has a subordinate node, activating the corresponding sensor to obtain the working parameters of the subordinate node if the abnormal node exists, detecting whether the subordinate node has an abnormality and locating the abnormality, including: S41: Detect whether the abnormal node has a subordinate node, and if so, start a sensor capable of obtaining working parameters of the subordinate node; Under normal circumstances, the sensors corresponding to the working parameters of the lower-level nodes are in the off state to save energy; S42: Similar to step S3, based on the transfer function of each lower-level node and the processed working parameters, it is determined whether each lower-level node has an abnormal operating state and whether there is a sensor abnormality, and a positioning method for the abnormal node is determined based on the node hierarchy to determine the specific location of the abnormal node; The node positioning method includes a binary positioning method and an interval positioning method; The binary location method is to determine the location of the abnormal node based on the transfer function of half of all nodes in the hierarchy, the input of the first node, and the ideal and actual output of the last node. The location of the abnormal node is initially determined, and then the part where the abnormal node is located is binary again. The above process is repeated until the specific location of the abnormal node is determined; The interval determination method is to divide the hierarchical nodes into a number of equal node segments (M), and determine the location of abnormal nodes by judging the transfer function of a node segment, the input of the first node, and the ideal and actual output of the last node. When an abnormality is found in a node segment, the node status within the node segment is judged in turn; Optionally, the highest level adopts a binary positioning method; the second highest level and the second lowest level adopt an interval positioning method, and the number of interval nodes is proportional to the level; The higher the node level, the more nodes it corresponds to. For example, the first-level node includes 3 nodes, p1-p3, and the second-level node p2 includes 9 nodes, p21, p22, p23, p24, p25, p26, p27, p28, and p29. In order to improve the speed of locating abnormalities in the second-level nodes, the interval judgment method can be used to set the number of interval nodes to 3 to quickly determine the location of abnormal nodes. The above technical solution can improve the speed of locating abnormal nodes. The method further includes S43: after determining the specific location of the abnormal node, shutting down the sensor corresponding to the working parameter of the abnormal node; S5: generating inspection tasks based on the abnormal location information and abnormal type, and dispatching unmanned inspection equipment to verify and maintain abnormal nodes, including: S51: When a sensor corresponding to the output data of the current node fails, the edge controller determines the location of the faulty sensor based on the current node. The edge controller calculates the ideal output value of the current node based on the output data of the previous node and the transfer function of the current node, and temporarily uses the ideal output value as the output data of the current node for continued monitoring of the operating status of the power equipment. When a sensor is abnormal, it may be impossible to accurately obtain the operating parameters of the power equipment, which may affect the stability of the power equipment monitoring system. For example, it may be impossible to normally generate corresponding control instructions based on the operating parameters. The solution in step S51 can further improve the stability of the power system operation. S52: When the current node is in an abnormal operating state, short-circuit the abnormal node and determine the location information of the abnormal node; S53: An inspection task is generated based on the abnormal location information. The unmanned inspection device moves to the abnormal location based on the inspection task to verify and maintain the abnormal information. Optionally, the method further includes: S54: after the unmanned inspection device moves to the corresponding position of the faulty sensor, canceling the operation of replacing the output data of the current node with the ideal output value in step S54, and collecting working parameters based on the same type of sensor carried by the unmanned inspection device itself for continued monitoring of the operating status of the power equipment. The unmanned inspection device can temporarily stay at the position of the faulty sensor as needed; Optionally, when the unmanned inspection device collects corresponding operating parameters based on its own sensors, it will connect to the edge controller through its own communication module to establish local communication, and send the collected operating parameters to the edge controller through local communication. The edge controller processes the acquired operating parameters based on the amplitude comparator and sends them to the edge controller, thereby reducing the pressure of wireless data transmission and storage, and also reducing its own computing power requirements. Optionally, when the unmanned inspection device collects corresponding operating parameters based on its own sensors, it can also download an amplitude comparator from the cloud server based on the location of the faulty sensor, and process the acquired operating parameters based on the amplitude comparator and upload them. Optionally, under normal circumstances, the unmanned inspection equipment will automatically inspect nodes at each level at a preset frequency, and at the same time verify the data collection accuracy of sensors at fixed locations based on its own sensors, so that people can quickly view and obtain the working performance of the sensors; The preset frequency = level size * g1 + number of nodes under each node in the level * g2 + aging degree of the equipment corresponding to each node * g3; where g1, g2, and g3 are preset proportional coefficients; S55: Staff can also obtain inspection task information through the preset terminal to conduct manual inspection; The step S6: updating the operating status of each node device to the virtual simulation model includes: S61: Based on the topological relationship and transfer function of the nodes, a virtual simulation model is established in the remote monitoring center; S62: Displaying the operating status of each node device in real time on the virtual simulation model; Optionally, the virtual simulation model includes different levels, and the staff can view the operating status of nodes at different levels in a hierarchical manner on the display interface, and can open the virtual simulation model corresponding to the lower-level node by double-clicking a node; As described in the background technology section, how to obtain the operating performance and error status of each trunk and branch road to achieve scientific and effective inspection and maintenance is also a major problem that needs to be solved in this field. To this end, the method further includes S63: calculating the cumulative error of each level node or each branch segment node, and generating and displaying the estimated maintenance time of each level node or each branch segment node based on the calculated error; The cumulative error calculation method of the nodes at each level is as follows: the ideal output of the last node of the level is obtained and calculated based on the input of the first node of the level and the transfer function of all nodes at the level, the actual output of the last node of the level is obtained by using a sensor, and the ideal and actual outputs of the last node of the level are compared to determine the cumulative error of the nodes at each level; the cumulative error of each branch node is similar to the above process; at the same time, the cumulative error of each node at each level or each branch node can be periodically updated and calculated while the operating parameters of the node are obtained by using a sensor, and the edge controller processes the obtained operating parameters based on the amplitude comparator and uploads them. The update period is positively correlated with the level of the node; Optionally, the staff can select a certain level or a certain branch node on the display interface to output the cumulative error of the corresponding level node or branch node, and view the estimated maintenance time of the level node or branch node.
[0012] According to another aspect of the present invention, a system for uploading and classifying data of power equipment is provided, which includes: a remote monitoring center, power equipment 1-n, a cloud server, an edge controller, unmanned inspection equipment, a preset terminal, and a data acquisition module; The remote monitoring center can divide power equipment into different levels and nodes based on the type, topology, and priority of the power equipment; determine the operating status of each node device and whether there is a sensor anomaly based on the transfer function and processed working parameters of each node; determine whether the abnormal node has a subordinate node, and if so, activate the corresponding sensor to obtain the working parameters of the subordinate node, detect whether the subordinate node has an anomaly and locate it; generate inspection tasks based on the location information and type of the anomaly, and dispatch unmanned inspection equipment to verify and maintain the abnormal node; and update the operating status of each node device to the virtual simulation model; The remote monitoring center includes interaction, display, storage, and alarm modules, which can complete the functions of command interaction, status display, data storage, and abnormal alarm; The data acquisition module is a sensor of different types, capable of collecting the working parameters of the node. As mentioned above, it can specifically be a current sensor, voltage sensor, power sensor, temperature sensor, humidity sensor, smoke concentration sensor, etc. The edge controller is arranged at the end of the power equipment; the edge controller can obtain the working parameters of the node by using the sensor, and the edge controller processes the obtained working parameters based on the amplitude comparator and sends them to the upper layer; The power equipment 1-n may be high-voltage power transmission equipment, transformers, low-voltage power transformation equipment, and switchgear; different equipment constitute different levels and nodes; The unmanned inspection equipment may be a drone or a robot, which includes control, storage, and movement modules and is equipped with corresponding types of sensors; The remote monitoring center can divide the power equipment into different levels and nodes based on the type, topology and priority of the power equipment, including: Preliminarily determine the priority of power equipment based on its type and topological relationship, and submit it to back-end staff for verification and adjustment; Divide the power equipment into different levels, each level includes different nodes, and each node corresponds to different power equipment; The edge controller can use sensors to obtain the operating parameters of the node. The edge controller processes the obtained operating parameters based on the amplitude comparator and sends them to the server, including: Using sensors to obtain the operating parameters of each node in the first level, and sending the operating parameters to the cache module of the edge controller; The cache module will regularly delete the data in it, and set different cache module deletion cycles for nodes of different levels and priorities, as well as nodes with different numbers of subordinate nodes; An amplitude comparator is used to detect the amplitude of the operating parameter in real time. When the amplitude of the operating parameter is greater than a first threshold or less than a second threshold, the amplitude comparator outputs a first signal. When the amplitude of the operating parameter is between the first threshold and the second threshold, the amplitude comparator outputs a second signal. The amplitude comparator is arranged at the end of the power equipment and can detect the amplitude of the working parameter; Optionally, the first threshold = node transfer function f(x) * node actual input x0 * k1; the second threshold = node transfer function f(x) * node actual input x0 * k2; k1 and k2 are the maximum amplitude proportional coefficient and the minimum amplitude proportional coefficient respectively; Optionally, k1=h1(a, b, c, d); k2=h2(a, b, c, d); Where a, b, c, and d represent the node's level, the priority of nodes at the same level, the number of subordinate nodes under the node, and the aging level of the node's corresponding equipment, respectively. h1 and h2 represent the calculation functions of the maximum amplitude proportional coefficient and the minimum amplitude proportional coefficient, respectively. In other words, k1 and k2 are related to the node's level, the priority of nodes at the same level, the number of subordinate nodes under the node, and the aging level of the node's corresponding equipment. The aging degree of the node device = the continuous operation time of the node device * c1 + the mean value of the node error per unit time * c2; The c1 and c2 are preset proportional coefficients; the mean value of the node error per unit time is calculated by integrating the difference between the ideal output and the actual output of the node and dividing the result by the time; The aging degree of the node device can be periodically updated and calculated while using sensors to obtain the node's operating parameters. The edge controller processes the obtained operating parameters based on the amplitude comparator and sends them to the server. The update period is positively correlated with the node's level. Optionally, the staff can also manually set and adjust the maximum amplitude proportional coefficient and the minimum amplitude proportional coefficient; Based on the time domain signals including the first signal and the second signal output by the amplitude comparator, the edge controller retains and marks the node operating parameters for the time period corresponding to the first signal, deletes the operating parameters for the time period corresponding to the second signal, and fills in the parameters with the time period and average amplitude. Specifically, the parameters include: Determine a first time period set T1={t1-t2, t3-t4, ...} corresponding to the amplitude comparator outputting the first signal and a second time period set T2={t2-t3, t4-t5, ...} corresponding to the amplitude comparator outputting the second signal; The working parameters of the node obtained by the sensor corresponding to the first time period set are retained and marked with the first signal label. The average value of the working parameters of the node obtained by the sensor corresponding to the second time period set is calculated. The working parameters of the corresponding set of the second time period set are deleted, and the working parameters of the node are replaced by the average value of the second time period set and the corresponding time period. Obtaining and combining the operating parameters of the first signal and the second signal for the corresponding time period, and sending the obtained operating parameters to the remote monitoring center; The remote monitoring center determines the operating status of each node device and whether there are any sensor anomalies based on the transfer function and processed working parameters of each node, including: During the trial operation phase, the input and output data samples of each node are obtained by changing the switch matrix state, and the transfer function of each node is identified based on the sample data of each node; Detecting the processed working parameters, and determining that there is a potential abnormality in the operating state of the current node when the first signal tag is found; Based on the topological relationship of the nodes, the output data of the previous node and the next node of the current node are obtained. The ideal output data of the next node is calculated through the transfer function of the current node, the next node and the output data of the previous node, and the actual output data of the next node is obtained. The ideal and actual output data of the next node are compared. When the deviation between the two is within the preset range, it is prompted that the sensor corresponding to the output data of the current node is faulty; when the deviation between the two exceeds the preset range, it is prompted that the operating status of the current node is abnormal. The determining whether the abnormal node has a subordinate node, activating the corresponding sensor to obtain the working parameters of the subordinate node if the abnormal node exists, detecting whether the subordinate node has an abnormality and locating the abnormality includes: Detect whether the abnormal node has a subordinate node, and if so, activate the sensor that can obtain the working parameters of the subordinate node; Based on the transfer function and processed working parameters of each lower-level node, it is judged whether each lower-level node has abnormal operation status and sensor abnormality, and the positioning method of the abnormal node is determined based on the node hierarchy to determine the specific location of the abnormal node; Optionally, the highest level adopts a binary positioning method; the second highest level and the second lowest level adopt an interval positioning method, and the number of interval nodes is proportional to the level; The method further includes: after determining the specific location of the abnormal node, shutting down the sensor corresponding to the working parameter of the abnormal node; The generation of inspection tasks based on the abnormal location information and abnormal type, and the dispatching of unmanned inspection equipment to perform verification and maintenance of abnormal nodes, include: When the sensor corresponding to the output data of the current node fails, the edge controller can determine the location of the faulty sensor based on the current node. The edge controller can calculate the ideal output value of the current node based on the output data of the previous node and the transfer function of the current node, and temporarily use it as the output data of the current node for continued monitoring of the operating status of the power equipment. When the current node is operating abnormally, the abnormal node is short-circuited and the location information of the abnormal node is determined; Generate inspection tasks based on abnormal location information. Unmanned inspection equipment moves to the abnormal location based on the inspection tasks to verify and maintain the abnormal information. The method further includes: after the unmanned inspection device moves to the corresponding position of the fault sensor, collecting working parameters based on the same type of sensors carried by the unmanned inspection device itself for continued monitoring of the operating status of the power equipment, and the unmanned inspection device can temporarily stay at the position of the fault sensor as needed; Optionally, when the unmanned inspection device collects corresponding operating parameters based on its own sensors, it will connect to the edge controller through its own communication module to establish local communication, and send the collected operating parameters to the edge controller through local communication. The edge controller processes the acquired operating parameters based on the amplitude comparator and sends them to the edge controller, thereby reducing the pressure of wireless data transmission and storage, and also reducing its own computing power requirements. Optionally, when the unmanned inspection device collects corresponding operating parameters based on its own sensors, it can also download an amplitude comparator from the cloud server based on the location of the faulty sensor, and process the acquired operating parameters based on the amplitude comparator and upload them. Optionally, under normal circumstances, the unmanned inspection device will automatically inspect nodes at each level at a preset frequency, and at the same time verify the data collection accuracy of sensors at fixed positions based on its own sensors; The preset frequency = level size * g1 + number of nodes under each node in the level * g2 + aging degree of the equipment corresponding to each node * g3; where g1, g2, and g3 are preset proportional coefficients; Optionally, staff can also obtain inspection task information through a preset terminal to conduct manual inspections; The updating of the operating status of each node device to the virtual simulation model includes: Based on the topological relationship and transfer function of the nodes, a virtual simulation model is established in the remote monitoring center; Display the operating status of each node device in real time on the virtual simulation model; The virtual simulation model includes different levels. The staff can view the running status of nodes at different levels in the display interface. By double-clicking a node, the virtual simulation model corresponding to the lower-level node can be opened. It also includes: calculating the cumulative error of each level node or each branch node, and generating and displaying the estimated maintenance time of each level node or each branch node based on the error; The cumulative error calculation method for the nodes at each level is as follows: the ideal output of the last node of the level is obtained and calculated based on the input of the first node of the level and the transfer function of all nodes in the level, the actual output of the last node of the level is obtained by using a sensor, and the ideal and actual outputs of the last node of the level are compared to determine the cumulative error of the nodes at each level. The cumulative error of each branch node is similar to the above process. The accumulated error is updated and calculated periodically, and the update period is positively correlated with the level of the node; Optionally, the staff can select a certain level or a certain branch node on the display interface to output the cumulative error of the corresponding level node or branch node, and view the estimated maintenance time of the level node or branch node.
[0013] The present invention provides a method and system for uploading and classifying data of power equipment, including: dividing the power equipment into different levels and nodes based on the type, topological relationship and priority of the power equipment; using sensors to obtain the working parameters of the nodes, and the edge controller processes the obtained working parameters based on the amplitude comparator and uploads them; judging the operating status of each node device and whether there is a sensor abnormality based on the transfer function of each node and the processed working parameters; judging whether the abnormal node has a lower-level node, and activating the corresponding sensor to obtain the working parameters of the lower-level node when it exists, detecting whether there is an abnormality in the lower-level node and locating it; generating inspection tasks based on the location information and type of the abnormality, and dispatching unmanned inspection equipment to calibrate and maintain the abnormal node; the present invention can realize accurate monitoring of the operating status of power equipment, avoid the occurrence of false alarms, and effectively ensure the safety and stability of the power system.
[0014] The above content is only the technical idea of the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.
Claims
1. A method for uploading and classifying data of electric power equipment, characterized in that: The following steps are included: S1: Divide the power equipment into different levels and nodes based on the type, topological relationship and priority of the power equipment; S2: Use sensors to obtain the working parameters of the node. The edge controller processes the obtained working parameters based on the amplitude comparator and sends them up. S3: judging the operating status of each node device and whether there is a sensor abnormality based on the transfer function of each node and the processed working parameters; S4: Determine whether the abnormal node has a subordinate node, and if so, activate the corresponding sensor to obtain the working parameters of the subordinate node, detect whether the subordinate node is abnormal, and locate it; S5: Generate inspection tasks based on the abnormal location information and abnormal type, and dispatch unmanned inspection equipment to verify and maintain abnormal nodes; S6: Update the operating status of each node device to the virtual simulation model.
2. The method according to claim 1, characterized in that S2: using the sensor to obtain the working parameters of the node, the edge controller processes the obtained working parameters based on the amplitude comparator and sends them up, including: S21: using sensors to obtain working parameters of each node in the first level, and sending the working parameters to a cache module of the edge controller; S22: using an amplitude comparator to detect the amplitude of the working parameter in real time; when the amplitude of the working parameter is greater than a first threshold or less than a second threshold, the amplitude comparator outputs a first signal; when the amplitude of the working parameter is between the first threshold and the second threshold, the amplitude comparator outputs a second signal; S23: The edge controller retains and marks the node working parameters of the time period corresponding to the first signal based on the time domain signal including the first signal and the second signal output by the amplitude comparator, deletes the working parameters of the time period corresponding to the second signal, and fills it with the time period and the average amplitude; S24: Obtain and combine the working parameters of the first signal and the second signal in the corresponding time period, and send the obtained working parameters to the remote monitoring center.
3. The method according to claim 2, characterized in that The first threshold value = node transfer function f(x)*node actual input x0*k1; the second threshold value = node transfer function f(x)*node actual input x0*k2; k1 and k2 are the maximum amplitude proportional coefficient and the minimum amplitude proportional coefficient respectively; The k1=h1(a, b, c, d); k2=h2(a, b, c, d); Where a, b, c, and d represent the node level, the priority of nodes at the same level, the number of subordinate nodes under the node, and the aging degree of the equipment corresponding to the node, respectively; h1 and h2 represent the calculation functions of the maximum amplitude proportional coefficient and the minimum amplitude proportional coefficient, respectively.
4. The method according to claim 3, characterized in that The step S23 specifically includes: S231: Determine a first time period set T1={t1-t2, t3-t4, ...} corresponding to the first signal output by the amplitude comparator and a second time period set T2={t2-t3, t4-t5, ...} corresponding to the second signal output by the amplitude comparator; S232: retain the working parameters of the node acquired by the sensor corresponding to the first time period set, and mark them with the first signal label, calculate the average value of the working parameters of the node acquired by the sensor corresponding to the second time period set, delete the working parameters of the corresponding set of the second time period set, and use the average value of the second time period set and the corresponding time period to replace the working parameters of the node.
5. The method according to claim 1, characterized in that S1: Based on the type, topological relationship and priority of the power equipment, the power equipment is divided into different levels and nodes, including: S11: Preliminarily determine the priority of power equipment based on its type and topological relationship, and submit it to the back-end staff for verification and adjustment; S12: Divide the power equipment into different levels, each level includes different nodes, and each node corresponds to different power equipment.
6. The method according to claim 1, characterized in that S3: judging the operating status of each node device and whether there is a sensor abnormality based on the transfer function of each node and the processed working parameters, including: S31: In the trial operation phase, the input and output data samples of each node are obtained by changing the switch matrix state, and the transfer function of each node is identified based on the sample data of each node; S32: Detecting the processed working parameters, and when the first signal tag is found, determining that there is a potential abnormality in the operating state of the current node; S33: Based on the topological relationship of the nodes, the output data of the previous node and the next node of the current node are obtained, the ideal output data of the next node is calculated through the transfer function of the current node, the next node and the output data of the previous node, the actual output data of the next node is obtained, and the ideal and actual output data of the next node are compared. When the deviation between the two is within the preset range, it is prompted that the sensor corresponding to the output data of the current node is faulty; when the deviation between the two exceeds the preset range, it is prompted that the operating status of the current node is abnormal.
7. A power equipment data uploading and classification processing system based on the method according to any one of claims 1 to 6, characterized in that: include: Remote monitoring center, power equipment 1-n, cloud server, edge controller, unmanned inspection equipment, preset terminal, data acquisition module; The remote monitoring center can divide the power equipment into different levels and nodes based on the type, topological relationship and priority of the power equipment; Based on the transfer function of each node and the processed working parameters, the operating status of each node device and whether there is a sensor abnormality are determined; Determine whether the abnormal node has a subordinate node. If so, activate the corresponding sensor to obtain the working parameters of the subordinate node, detect whether the subordinate node is abnormal and locate it; generate inspection tasks based on the abnormal location information and abnormal type, and dispatch unmanned inspection equipment to verify and maintain the abnormal node; Update the operating status of each node device to the virtual simulation model; The edge controller is arranged at the end of the power equipment; the edge controller can obtain the working parameters of the node by using the sensor, and the edge controller processes the obtained working parameters based on the amplitude comparator and sends them up.
8. The system according to claim 7, characterized in that The edge controller can use the sensor to obtain the working parameters of the node. The edge controller processes the obtained working parameters based on the amplitude comparator and sends them up, including: Using sensors to obtain the working parameters of each node in the first level, and sending the working parameters to the cache module of the edge controller; The amplitude comparator is used to detect the amplitude of the working parameter in real time. When the amplitude of the working parameter is greater than a first threshold or less than a second threshold, the amplitude comparator outputs a first signal. When the amplitude of the working parameter is between the first threshold and the second threshold, the amplitude comparator outputs a second signal. The edge controller retains and marks the node working parameters of the time period corresponding to the first signal based on the time domain signal including the first signal and the second signal output by the amplitude comparator, deletes the working parameters of the time period corresponding to the second signal, and fills it with the time period and the average amplitude; The working parameters of the first signal and the second signal corresponding to the time period are obtained and combined, and the obtained working parameters are sent to the remote monitoring center.
9. The system according to claim 8, characterized in that The cache module will delete the data therein periodically, and different cache module deletion cycles are set for nodes of different levels and priorities, and nodes with different numbers of subordinate nodes.
10. The system according to claim 8, characterized in that After the unmanned inspection device moves to the corresponding position of the fault sensor, the working parameters are collected based on the same type of sensors carried by the unmanned inspection device itself for continued monitoring of the operating status of the power equipment. The unmanned inspection device can temporarily stay at the position of the fault sensor as needed.